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Paper details
Number 3 - September 2021
Volume 31 - 2021
Forecasting models for chaotic fractional-order oscillators using neural networks
Kishore Bingi, B Rajanarayan Prusty
Abstract
This paper proposes novel forecasting models for fractional-order chaotic oscillators, such as Duffing’s, Van der Pol’s,
Tamaševičius’s and Chua’s, using feedforward neural networks. The models predict a change in the state values which
bears a weighted relationship with the oscillator states. Such an arrangement is a suitable candidate model for out-of-sample
forecasting of system states. The proposed neural network-assisted weighted model is applied to the above oscillators. The
improved out-of-sample forecasting results of the proposed modeling strategy compared with the literature are comprehensively analyzed. The proposed models corresponding to the optimal weights result in the least mean square error (MSE) for all the system states. Further, the MSE for the proposed model is less in most of the oscillators compared with the one reported in the literature. The proposed prediction model’s out-of-sample forecasting plots show the best tracking ability to approximate future state values.
Keywords
chaotic oscillators, data-driven forecasting, fractional-order systems, model-free analysis, neural networks, time-series prediction